The interplay of covalency, hydrogen bonding, and dispersion leads to a long range chiral network: The example of 2-butanol

The assembly of complex structures in nature is driven by an interplay between several intermolecular interactions, from strong covalent bonds to weaker dispersion forces. Understanding and ultimately controlling the self-assembly of materials requires extensive study of how these forces drive local nanoscale interactions and how larger structures evolve. Surface-based self-assembly is particularly amenable to modeling and measuring these interactions in well-defined systems. This study focuses on 2-butanol, the simplest aliphatic chiral alcohol. 2-butanol has recently been shown to have interesting properties as a chiral modifier of surface chemistry; however, its mode of action is not fully understood and a microscopic understanding of the role non-covalent interactions play in its adsorption and assembly on surfaces is lacking. In order to probe its surface properties, we employed high-resolution scanning tunneling microscopy and density functional theory (DFT) simulations. We found a surprisingly rich degree of enantiospecific adsorption, association, chiral cluster growth and ultimately long range, highly ordered chiral templating. Firstly, the chiral molecules acquire a second chiral center when adsorbed to the surface via dative bonding of one of the oxygen atom lone pairs. This interaction is controlled via the molecule's intrinsic chiral center leading to monomers of like chirality, at both chiral centers, adsorbed on the surface. The monomers then associate into tetramers via a cyclical network of hydrogen bonds with an opposite chirality at the oxygen atom. The evolution of these square units is surprising given that the underlying surface has a hexagonal symmetry. Our DFT calculations, however, reveal that the tetramers are stable entities that are able to associate with each other by weaker van der Waals interactions and tessellate in an extended square network. This network of homochiral square pores grows to cover the whole Au(111) surface. Our data reveal that the chirality of a simple alcohol can be transferred to its surface binding geometry, drive the directionality of hydrogen-bonded networks and ultimately extended structure. Furthermore, this study provides the first microscopic insight into the surface properties of this important chiral modifier and provides a well-defined system for studying the network's enantioselective interaction with other molecules

Novelty Enhances Visual Perception

The effects of novelty on low-level visual perception were investigated in two experiments using a two-alternative forced-choice tilt detection task. A target, consisting of a Gabor patch, was preceded by a cue that was either a novel or a familiar fractal image. Participants had to indicate whether the Gabor stimulus was vertically oriented or slightly tilted. In the first experiment tilt angle was manipulated; in the second contrast of the Gabor patch was varied. In the first, we found that sensitivity was enhanced after a novel compared to a familiar cue, and in the second we found sensitivity to be enhanced for novel cues in later experimental blocks when participants became more and more familiarized with the familiar cue. These effects were not caused by a shift in the response criterion. This shows for the first time that novel stimuli affect low-level characteristics of perception. We suggest that novelty can elicit a transient attentional response, thereby enhancing perception

Emotional cues enhance the attentional effects on spatial and temporal resolution

In the present study, we demonstrated that the emotional significance of a spatial cue enhances the effect of covert attention on spatial and temporal resolution (i.e., our ability to discriminate small spatial details and fast temporal flicker). Our results indicated that fearful face cues, as compared with neutral face cues, enhanced the attentional benefits in spatial resolution but also enhanced the attentional deficits in temporal resolution. Furthermore, we observed that the overall magnitudes of individuals’ attentional effects correlated strongly with the magnitude of the emotion × attention interaction effect. Combined, these findings provide strong support for the idea that emotion enhances the strength of a cue’s attentional response

A compendium and functional characterization of mammalian genes involved in adaptation to Arctic or Antarctic environments

Many mammals are well adapted to surviving in extremely cold environments. These species have likely accumulated genetic changes that help them efficiently cope with low temperatures. It is not known whether the same genes related to cold adaptation in one species would be under selection in another species. The aims of this study therefore were: to create a compendium of mammalian genes related to adaptations to a low temperature environment; to identify genes related to cold tolerance that have been subjected to independent positive selection in several species; to determine promising candidate genes/pathways/organs for further empirical research on cold adaptation in mammals

Learning deterministic probabilistic automata from a model checking perspective

Probabilistic automata models play an important role in the formal design and analysis of hard- and software systems. In this area of applications, one is often interested in formal model-checking procedures for verifying critical system properties. Since adequate system models are often difficult to design manually, we are interested in learning models from observed system behaviors. To this end we adopt techniques for learning finite probabilistic automata, notably the Alergia algorithm. In this paper we show how to extend the basic algorithm to also learn automata models for both reactive and timed systems. A key question of our investigation is to what extent one can expect a learned model to be a good approximation for the kind of probabilistic properties one wants to verify by model checking. We establish theoretical convergence properties for the learning algorithm as well as for probability estimates of system properties expressed in linear time temporal logic and linear continuous stochastic logic. We empirically compare the learning algorithm with statistical model checking and demonstrate the feasibility of the approach for practical system verification

Use of attribute association error probability estimates to evaluate quality of medical record geocodes

BACKGROUND: The utility of patient attributes associated with the spatiotemporal analysis of medical records lies not just in their values but also the strength of association between them. Estimating the extent to which a hierarchy of conditional probability exists between patient attribute associations such as patient identifying fields, patient and date of diagnosis, and patient and address at diagnosis is fundamental to estimating the strength of association between patient and geocode, and patient and enumeration area. We propose a hierarchy for the attribute associations within medical records that enable spatiotemporal relationships. We also present a set of metrics that store attribute association error probability (AAEP), to estimate error probability for all attribute associations upon which certainty in a patient geocode depends. METHODS: A series of experiments were undertaken to understand how error estimation could be operationalized within health data and what levels of AAEP in real data reveal themselves using these methods. Specifically, the goals of this evaluation were to (1) assess if the concept of our error assessment techniques could be implemented by a population-based cancer registry; (2) apply the techniques to real data from a large health data agency and characterize the observed levels of AAEP; and (3) demonstrate how detected AAEP might impact spatiotemporal health research. RESULTS: We present an evaluation of AAEP metrics generated for cancer cases in a North Carolina county. We show examples of how we estimated AAEP for selected attribute associations and circumstances. We demonstrate the distribution of AAEP in our case sample across attribute associations, and demonstrate ways in which disease registry specific operations influence the prevalence of AAEP estimates for specific attribute associations. CONCLUSIONS: The effort to detect and store estimates of AAEP is worthwhile because of the increase in confidence fostered by the attribute association level approach to the assessment of uncertainty in patient geocodes, relative to existing geocoding related uncertainty metrics

Financing of U.S. Biomedical Research and New Drug Approvals across Therapeutic Areas

We estimated U.S. biomedical research funding across therapeutic areas, determined the association with disease burden, and evaluated new drug approvals that resulted from this investment.We calculated funding from 1995 to 2005 and totaled Food and Drug Administration approvals in eight therapeutic areas (cardiovascular, endocrine, gastrointestinal, genitourinary, HIV/AIDS, infectious disease excluding HIV, oncology, and respiratory) primarily using public data. We then calculated correlations between funding, published estimates of disease burden, and drug approvals. Financial support for biomedical research from 1995 to 2005 increased across all therapeutic areas between 43% and 369%. Industry was the principal funder of all areas except HIV/AIDS, infectious disease, and oncology, which were chiefly sponsored by the National Institutes of Health (NIH). Total (rho = 0.70; P = .03) and industry funding (rho = 0.69; P = .04) were correlated with projected disease burden in high income countries while NIH support (rho = 0.80; P = .01) was correlated with projected disease burden globally. From 1995 to 2005 the number of new approvals was flat or declined across therapeutic areas, and over an 8-year lag period, neither total nor industry funding was correlated with future approvals.Across therapeutic areas, biomedical research funding increased substantially, appears aligned with disease burden in high income countries, but is not linked to new drug approvals. The translational gap between funding and new therapies is affecting all of medicine, and remedies must include changes beyond additional financial investment